I’ve written a few times that perhaps the biggest unsolved problem in robocars is how to know we have made them safe enough. While most people think of that in terms of government certification, the truth is that the teams building the cars are very focused on this, and know more about it than any regulator, but they still don’t know enough. The challenge is going to be convincing your board of directors that the car is safe enough to release, for if it is not, it could ruin the company that releases it, at least if it’s a big company with a reputation.

The first VENTURER trials set out to investigate ‘takeover’ (time taken to reengage with vehicle controls) and ‘handover’ (time taken to regain a baseline/normal level of driving behaviour and performance) when switching frequently between automated and manual driving modes within urban and extra-urban settings. This trial is believed to be the first to directly compare handover to human driver-control from autonomous mode in both simulator and autonomous road vehicle platforms.

SoftBank, the giant telecom company, is venturing out into the world of robotics and transportation services. DealStreet Asia said that SoftBank is trying to transform itself into the ‘Berkshire Hathaway of the tech industry’ with the recent launch of a $100 billion technology fund.

UPDATED 5/24/17: SoftBank’s acquisition of 4.9% of the outstanding shares of Nvidia Corp.

I was recently asked about the differences between RADAR and LIDAR. I gave the generic answer about LIDAR having higher resolution and accuracy than RADAR. And RADAR having a longer range and performing better in dust and smokey conditions. When prompted for why RADAR is less accurate and lower resolution, I sort of mumbled through a response about the wavelength. However, I did not have a good response, so this post will be my better response.

As the last in our series of blog posts on machine learning in research, we spoke to Dr Nathan Griffiths to find out more about machine learning in transport. Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or “driverless cars”) has been supported by a Royal Society University Research Fellowship.

The field of transportation is undergoing a seismic shift with the introduction of autonomous driving — or computer-driven cars. Computer vision scientist and Mobileye co-founder Amnon Shashua PhD ’93 described the challenges associated with this technology in a talk last month hosted by MIT’s Center for Brains, Minds and Machines (CBMM).

If you take humans out of the driving seat, could traffic jams, accidents and high fuel bills become a thing of the past? As cars become more automated and connected, attention is turning to how to best choreograph the interaction between the tens or hundreds of automated vehicles that will one day share the same segment of Europe’s road network.

Automated cars are hurtling towards us at breakneck speed, with all-electric Teslas already running limited autopilot systems on roads worldwide and Google trialling its own autonomous pod cars. However, before we can reply to emails while being driven to work, we have to have a foolproof way to determine when drivers can safely take control and when it should be left to the car.

Imagine a future where self-driving cars, trains and buses are all seamlessly connected through an app, where traffic jams are a thing of the past and redundant car parks have been turned into green spaces. This could be the world we live in by 2030, says Cathis Elmsäter-Svärd, Chairwoman of Drive Sweden and a member of the Global Future Council on Mobility, in this interview.

I generally pay very little attention when companies issue a press release about an “alliance.” It’s usually not a lot more than a press release, unless there are details on what will actually be built. The recent announcement that Uber plans to buy some self-driving cars from Daimler/Mercedes is mostly just such an announcement.

Robocars are broadly going to be a huge boon for many people with disabilities, especially disabilities that make it difficult to drive or those that make it hard to get in and out of vehicles. Existing disability regulations and policies were written without robocars in mind, and there are probably some improvements that need to be made.

In this episode, Audrow Nash interviews Christoph Stiller from the Karlsruhe Institute of Technology. Stiller speaks about the sensors required for various level of autonomous driving, as well as the ethics of autonomous cars, and his experience in the Defense Advanced Research Projects Agency (DARPA) Grand Challenge.